Basic Information

  • COVID-19 info: We will follow the general ETH regulations, which are permanently updated, and communicate if there are updates. There are no course-specific exceptions. Right now this means we start with zoom lectures, make recordings available (details by email), and hope for a regular, physical midterm.
  • READ: Course description, prerequisites, goals, integrity
  • Read the slides of the first lecture
  • FAQs
  • Course number: 263-0007, 8 credits
  • Spring 2021, lectures: M 10:15-12:00, HG F3; Th 9:15-10:00 HG F3; occasional substitute lectures: W 14:15-16:00 ETF C1
  • Instructor: Markus Püschel (CAB H69.3, pueschel at inf), Ce Zhang (ce.zhang at inf)
  • Head TA:
    • Joao Rivera (JR)
  • TAs:
    • Eliza Wszola (EW)
    • Konstantin Taranov (KT)
    • Theodoros Theodoridis (TT)
  • Mailing lists:
    • For technical questions: (emails to this address go to the lecturer and all TAs)
    • Forum to find project partner: (emails go to all students who have no partner yet and to Head TA)
  • Office Hours: (No office hours after April 23)
    • Mon 12:30-14:00: Theodoros
    • Tue 13:30-15:00: Eliza
    • Wed 14:00-15:30: Konstantin
    • Fri 12:30-14:00: Joao

Time Line

This list can be subject to minor changes, which would be announced in a timely manner.

Fr 12.03. Project team and project registered in the project system
Th 11.03. HW1 due
Th 18.03. HW2 due
Th 01.04 HW3 due
Sa 17.04. HW4 due
Wed 21.04. Midterm
week of 03.05 1st one-on-one project meeting
week of 24.05. 2nd one-on-one project meeting
week of 07.06. Project presentations
Fr 25.06. Project report due


  • 40% research project
  • Topic: Very fast, ideally adaptive implementation and associated performance analysis for a numerical problem
  • Team up in groups of four: register in the project system
  • March 12th: find team, find a problem (tip: look at the prior courses linked above under Teaching for examples)
  • Finding a problem: either pick from the below list (6 teams max per topic) or suggest a project to MP for approval (email MP with paper containing algorithm). If you pick from this list, the decision is final and cannot be changed.
    1. Arbitrary precision ball arithmetic
    2. Hierarchical density based clustering (hdbscan)
    3. Relational queries over bit-parallel database layout
    4. Sphere tracing, example input scenes
    5. T-stochastic neighbour embedding (t-SNE)
  • Once project is fixed: add it in the project system to your team
  • Complete “milestones” during the semester and enter them in the project system
  • Later in semester: One or two 1 hour one-on-one meetings with a project supervisor
  • Write 8 page standard conference paper (template is provided below)
  • Give short presentation end of semester
  • 30% midterm
  • 30% homework
    • Exercises on code analysis
    • Implementation exercises
      • study the effect of program optimizations, compilers, special instructions, etc.
      • write and submit C code & create runtime/performance plots
    • Some templates will be provided
    • All homeworks are single-student homeworks (read integrity rules)
  • There is no final exam

Research Project

  • All projects have to be registered in our project system. This site contains a rough structure for your project and is also used later for updates.
  • How it works:
    • Weeks without homeworks should be used to work on the project
    • You select a numerical algorithm and create a correct (tested) implementation in C
    • You determine the arithmetic cost, measure the runtime and performance
    • You profile the implementation to find the parts in which most the runtime spent
    • Focusing on these you apply various optimization techniques from this class
    • You repeat the previous steps to create various versions with (hopefully) continuously better runtime
    • You use (exclusively) a repository that we provide to you
    • You analyze and reason about the performance behavior
    • You write a paper about your work and give a presentation
  • Paper:
    • Maximal 8 pages (hard limit) without references, conference style, template and instructions below
    • Everybody reads this: report.pdf
    • Latex source:
    • Due date: 25.06 (in your git repository)
    • Name: (Team ID) + _report.pdf, e.g. 07_report.pdf
  • Presentation
  • Rough timeline
    • Start project work: any time, the earlier the better
    • Assignment project advisor: around mid April
    • One-on-one project meetings: two in May, see above
# Predefined Topics Supervisor/s
1 Arbitrary precision ball arithmetic JR
2 Hierarchical density based clustering (hdbscan) EW
3 Relational queries over bit-parallel database layout CZ
4 Sphere tracing TT
5 T-stochastic neighbour embedding (t-SNE) KT
# Proposed Topics Supervisor/s
6 Optimizing the EPIC System in the SCION Infrastructure KT
7 Gilbert–Johnson–Keerthi distance algorithm JR
8 Polynomial multipoint evaluation JR
9 Wave Function Collapse EW
10 SPIHT Image Compression EW
11 Fortune’s Algorithm CZ
12 Flow algorithms with emphasis on Edmonds-Karp EW
13 Optimization of a FLIP algorithm EW
14 SURF: Speeded Up Robust Features CZ
15 Censorship-avoiding high-speed EC (Elligator with Curve1174) KT



  • all the material up to then is fair game but the overwhelming part will be what was covered in the homeworks
  • you can study previous exams, all on the website
  • no books, notes, calculators, laptops, cell phones, or other electronic devices are allowed
  • once the exam has finished, you should leave the building

Previous exams:


Late policy: No deadline extensions, but you have 3 late days. You can use at most 2 on one homework. For example, submitting 20 minutes or 7 hours late costs one late day.

We will be using Moodle for the homeworks.

It may help to look at the homeworks of previous iterations of this course (above under the Teaching tab).

Homework Deadline Solution
Homework 0 as soon as possible  
Homework 1 Th March 11th, 5pm Homework 1
Homework 2 Th March 18th, 5pm Homework 2
Homework 3 Th April 1st, 5pm programming exercise auto-assessed
Homework 4 Sa April 17th, 5pm Homework 4

Lectures Plan

Date Content Other Material
22.02 Course motivation, overview, organization  
25.02 Cost analysis and performance  
01.03 Intel Haswell architecture/microarchitecture, operational intensity Intel optimization manual, Section 2.2
04.03 Instruction level parallelism  
08.03 Compiler limitations, benchmarking  
11.03 SIMD vector instructions, AVX Intel intrinsics guide, Agner Fog’s instruction tables, see also the recent uops
15.03 SIMD vector instructions, AVX  
18.03 Compiler vectorization  
22.03 Locality, caches  
25.03 Caches, blocking MMM  
29.03 Roofline model, Linear algebra libraries, BLAS, ATLAS  
01.04 Fast MMM  
12.04 Fast MMM continued, register renaming, virtual memory  
15.04 Sparse linear algebra, sparse MVM  
19.04 Discrete/fast Fourier transform  
26.04 Fast FFT, FFTW  
10.05 Spiral: DSL-based program generation for performance